{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5f152d02-c872-4d4d-a2fd-041b7de13300",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "**Pedro Rivero**  \n",
    "Quantum Developer *@ IBM Quantum*  \n",
    "pedro.rivero@ibm.com "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "940e27cb-2f9b-42ac-8007-8684a46165af",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Qiskit Estimator primitive\n",
    "\n",
    "The Estimator primitive lets you calculate expectation values of quantum operators by specifying circuits and observables.  \n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "    <i class=\"fas fa-info-circle\"></i>\n",
    "    <b>NOTICE</b><br/> \n",
    "    Official interface as of Qiskit Terra 0.22.0.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93bd23e2-5ec5-43d3-9023-5dd292d70687",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Declaring inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2701db75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1792.29x200.667 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qiskit.circuit.random import random_circuit\n",
    "\n",
    "circuit = random_circuit(2, 4, seed=1).decompose(reps=1)\n",
    "display(circuit.draw(\"mpl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4a38ada-07b3-48ca-b89a-19d288abf575",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.quantum_info import SparsePauliOp\n",
    "\n",
    "observable = SparsePauliOp(\"ZZ\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a7182e-8ea1-4d13-96b3-1f3f223c5163",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Running experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "525ae9d8-c1f1-4efe-a007-3325328621d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.primitives import Estimator\n",
    "\n",
    "estimator = Estimator()\n",
    "job = estimator.run(circuit, observable)\n",
    "simulation = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b0c5a9a8-4033-4e0e-af42-1844b91ce104",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"values\": [\n",
      "    0.9999999999999984\n",
      "  ],\n",
      "  \"metadata\": [\n",
      "    {}\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "from zne.utils.serialization import EstimatorResultEncoder\n",
    "\n",
    "print(EstimatorResultEncoder.dumps(simulation, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ce81c2-b637-4da7-81b2-caf5e28469b2",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Zero Noise Extrapolation (ZNE)\n",
    "![ZNE](../media/zne.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d9255d7-f014-47c0-b426-aefad945ee29",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Overall workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "558f770b-89de-4c3e-af52-1447a7345733",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (1) Building a ZNE Estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5650a0fa-aab6-4d7f-be9e-78ab4e1d5cce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.primitives import BackendEstimator\n",
    "from qiskit.providers.fake_provider import FakeNairobi  # We need noise!\n",
    "from zne import zne\n",
    "\n",
    "ZNEEstimator = zne(BackendEstimator)  # Any implementation of BaseEstimator is valid\n",
    "backend = FakeNairobi()\n",
    "estimator = ZNEEstimator(backend=backend)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "291672ae-fd30-45b7-9e00-e1614c33bcc1",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (2) Configuring ZNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "07745488",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zne import ZNEStrategy\n",
    "from zne.noise_amplification import LocalFoldingAmplifier\n",
    "from zne.extrapolation import PolynomialExtrapolator\n",
    "\n",
    "## NOISE FACTORS\n",
    "noise_factors = range(1,9)\n",
    "\n",
    "## NOISE AMPLIFIER\n",
    "noise_amplifier = LocalFoldingAmplifier(\n",
    "    gates_to_fold=2,\n",
    ")\n",
    "\n",
    "## EXTRPOLATOR\n",
    "extrapolator = PolynomialExtrapolator(\n",
    "    degree=2,\n",
    ")\n",
    "\n",
    "## ZNE STRATEGY\n",
    "zne_strategy = ZNEStrategy(\n",
    "    noise_factors=noise_factors,\n",
    "    noise_amplifier=noise_amplifier,\n",
    "    extrapolator=extrapolator,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66b7452c-2cb8-49a0-96aa-7b9c97710842",
   "metadata": {
    "tags": []
   },
   "source": [
    "### (3) Running a ZNE experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18d7c815-7ef3-46c6-a1a9-189cf5c9062e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"values\": [\n",
      "    0.9524972098214297\n",
      "  ],\n",
      "  \"metadata\": [\n",
      "    {\n",
      "      \"zne\": {\n",
      "        \"noise_amplification\": {\n",
      "          \"noise_amplifier\": \"<LocalFoldingAmplifier:{'gates_to_fold': 2, 'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\",\n",
      "          \"noise_factors\": [\n",
      "            1,\n",
      "            2,\n",
      "            3,\n",
      "            4,\n",
      "            5,\n",
      "            6,\n",
      "            7,\n",
      "            8\n",
      "          ],\n",
      "          \"values\": [\n",
      "            0.908203125,\n",
      "            0.875,\n",
      "            0.853515625,\n",
      "            0.76171875,\n",
      "            0.80078125,\n",
      "            0.712890625,\n",
      "            0.71484375,\n",
      "            0.673828125\n",
      "          ],\n",
      "          \"variance\": [\n",
      "            0.17516708374023438,\n",
      "            0.234375,\n",
      "            0.2715110778808594,\n",
      "            0.4197845458984375,\n",
      "            0.3587493896484375,\n",
      "            0.4917869567871094,\n",
      "            0.4889984130859375,\n",
      "            0.5459556579589844\n",
      "          ],\n",
      "          \"shots\": [\n",
      "            1024,\n",
      "            1024,\n",
      "            1024,\n",
      "            1024,\n",
      "            1024,\n",
      "            1024,\n",
      "            1024,\n",
      "            1024\n",
      "          ]\n",
      "        },\n",
      "        \"extrapolation\": {\n",
      "          \"extrapolator\": \"<PolynomialExtrapolator:{'degree': 2}>\"\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "job = estimator.run(circuit, observable, zne_strategy=zne_strategy)\n",
    "result = job.result()\n",
    "\n",
    "print(EstimatorResultEncoder.dumps(result, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "062351ad-3afa-46ea-88e0-72fc15d2f4d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.rcParams[\"figure.dpi\"] = 300\n",
    "plt.rcParams.update({\"text.usetex\": True, \"font.family\": \"Computer Modern Roman\"})\n",
    "\n",
    "############################  DATA  ############################\n",
    "exact = simulation.values[0]\n",
    "mitigated = result.values[0]\n",
    "noise_factors = result.metadata[0][\"zne\"][\"noise_amplification\"][\"noise_factors\"]\n",
    "noisy_values = result.metadata[0][\"zne\"][\"noise_amplification\"][\"values\"]\n",
    "\n",
    "############################  PLOT  ############################\n",
    "plt.rcParams[\"figure.figsize\"] = (5,3.5)\n",
    "plt.grid(which='major',axis='both')\n",
    "\n",
    "plt.plot([0, noise_factors[-1]], [exact, exact], \"--\", label=f\"Exact\", color=\"#000000\")\n",
    "plt.scatter(0, mitigated, label=f\"Mitigated\", marker=\"x\", color=\"#785ef0\")\n",
    "if noise_factors[0] == 1:\n",
    "    plt.scatter(\n",
    "        noise_factors[0], noisy_values[0], \n",
    "        label=f\"Unmitigated\", marker=\"s\", color=\"#dc267f\",\n",
    "    )\n",
    "plt.plot(\n",
    "    noise_factors, noisy_values, \n",
    "    label=f\"Amplified\", marker=\"o\", color=\"#dc267f\",\n",
    ")\n",
    "\n",
    "plt.title(\"Zero noise extrapolation\")\n",
    "plt.xlabel(\"Noise Factor ($n$)\")\n",
    "plt.ylabel(f\"Expectation Value ($\\langle ZZ \\\\rangle$)\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0b07ac9-21cc-4dc2-81a0-da9009b60441",
   "metadata": {
    "tags": []
   },
   "source": [
    "## ZNE strategies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fa0bc73-93ee-486e-b420-56598c0d3e15",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Noise Amplification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b92f2728-65a7-4fbe-8c2e-8568904b6dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1865.22x602 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "circuit = random_circuit(2, 2, seed=0).decompose(reps=1)\n",
    "display(circuit.draw(\"mpl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8eaaf703-f20e-4d50-8824-b562a551effb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vTz2/MisPuljz7R/Py5dmtm0JtvDuuZlw9uQD+z+a+1ctbrOvL3369uO2CLv7wflZsWZJ/qQdF2BoLC9vq7qC3strXx/8HKqzeVsRcvftU3Ulvdtmx0Bl9uxLajuSEUOqroQT5XyARvfya1VX0Hu9/Fpy/uiqqwAAyhC00uO0tLRk7ty5be5bsGBBqX1cdtllh91nRivttX9E+f6LExPOPnbb6dd2bMm2nbWcUmL9o3mzl2TB3XNz50+Kmdd3Ly8GFlxzZdHu6+4Hb8/KNUvzzS+tOOZ+tu2s5S8W33jg9oSzJ+eaK4+8hvHBXti8NqcNGt7uemkcr+6suoLey2tfH/wcqrN3X7JjdzJkYNWV9G6OgWq9ulPQ2hM4H6CR7dmb7Hi96ip6r1d3VV0BAFCWoJUeZ9asti2P5s2bl6amplL7ePjhh9vc7q3rs+7atStr166tuozj2rPjpCQTqy7jgO4a1X3jNfNy/dU35+4H52flmiW5b9WduXv5/AwZ1JRLJ0zPvNlLjjsS/T8tvrFNy6/2tAhLiotHo4cfvxV3VVavXp3+g9+ouoyG9PzGc5KcdsL76d83Of3UE6/nWE5vx4Xo9mxzol5+rZiFdKJ2vZE8+thv0r+vvrVVanl+VJLTT3g/joGOWfn4UxkxyOJkVfrty8OSnHXC++lNx0DSecfBr1c/kx2bTCVrj3o7BziY84H64/yg/V7b3XnHVm96L+is94Gnn38lv+73wonvCCrwwp49VZdAHVmzek229xc/Ud748eMzcGBjjcD2P50eZf78+Vm5sm1bptmzjz8a91CLFrU9uWzvbNalS5fm9ttvz8qVK9PS0pKmpqY0Nzfnuuuuy8yZM9u1Rmw9Wbt2bSZNmlR1Gcc1atjY/PVXnq66jEoMGdSU6z54U6774E3H3/gQK1cvzU9XvbWO8Q0fnXfctmL7vbi5JeePubz0c3aXD3/4Q9n4yvqqy2hI1/3fy3Pm+CtOeD+nn5r8u491QkEn6IYPdP1z/Nn3kxe3ds6+3j/92mzdVH69NjrP73zhf+X8d//uCe/HMdAx//jzv5/1q37UOTujQ6781L/P1E/9Xye8n950DCSddxz80f99a1Yt+csT31Ev0JvPAQ7mfKB9nB+035lvuzLX/fsHOmVfvem9oLPeB/5h2c/zhT+/9sR3BFUYNTInfet/VF0FdeJDH/5QsnFT1WXQgB5//PFccMEFVZdRSt+qC4DOdGjL4JkzZ5aezZocPqP1eOuzrly5MuPHjz+w3aJFi/LKK69kxYoVue6663LLLbdk/PjxmTNnTulaoKv8h79+a/b3hLMnt/vizLadtax5fuVxR8fTmAYPHVV1Cb3a4KFnVl1Cr+cYqJZjoHqDm/wMqnSKY4Bu5HyAI/FZqFo+CwFA4xG00mPMnz8/tVqtzX3XXXdd6f20tLQctp8jrdm63+LFizNlypS0tLRkyZIlWbRoUSZPnnxgNutNN92UV155JdOnT8/8+fMzZcqU0jVBZ/vG4jltWoT9wcz2r2N894PF+k+nDrYmU0/Ur/+Aqkvo1fqd5PWvmmOgWv36n1x1Cb2eY6BafR0DdBPnAxyN94FqOR8AgMYjaKXHmDdv3mH3tbfl78GWLl3a5nZzc/NRZ8WuXLnywJqwt99++zGfb8mSJWlubs7KlSuPO0MWutLK1Utz9/L5B25/5gM3ZcKY9q1DvGFzS/72x7ckSYYMbOqK8qhY334nVV1Cr+b1r56fQbW8/tXzM6hWP68/3cD5AMfSt7/fQ1XyPgwAjUfQSo/Q0tKSlpa2a9rtn1Va1pIlS9rcPlZ4uj9kbW5ubtdasPvD4KVLl2bx4sXH2Rq6xje+81YL69HDm3PjNYcPUjiS+1ctzhdum3Jg5LsR7D1Va9UF9G6tXv+qtToGKub1r56fQZX8DqI7OB/gmHwerZbXHwAaTv+qC4DOcKTQsiNtg5PDZ7Qebfbp4sWLD4S77V17debMmQe+nzt3bpvb0B0W3D03G7a8NSjhfRfNzMrVS4+47bZdtby2Y0vWPLciK9csbfO4JNZk6qH27d1TdQm92r59Xv+qtToGKuV3UPX8DKrl9aerOR/gePweqpbzAQBoPIJWeoRDZ6EmHWsbfKT1WY+2n1tuuaVDzzV9+vQsXbo0LS0tWbp0aYfqhI566vmVbW7f+ZNbc+dPbu3Qvoxg75n2vrGr6hJ6Na9/9fb4GVRq757dVZfQ6+15fWfVJfRqjgG6mvMBjsfn0Wp5/QGg8Qha6REOnYWaFK2DT3Q/R1uftaWlJStXrmyzXXsdvO3x1nWFzjZv9uGDEuBgO7ZuzGlnnFd1Gb3W9tqLVZfQ6+3Y6mdQpe2vbKi6hF5vx6sbqy6hV9vhfYAu5nyA49mx1ftAlZwPAEDjEbTS8A6dgZp0LGRN2r8+66GBbJm1YA/e9kgBcT0ZP358Hn/88arLOK49O07KS/dVXQX15Ec/uif9B79RdRkN6Z5152T91hPfz8uvJX/2/RPfz7GcPiS54QPH3mbhT5KXt3VtHS+/1nn7un/Z99O/r3WZqrT8+VFZ9dKJ78cx0DF//d+/meGDzOir0m9fHpafPXfi++lNx0DSecfBV//93Iz7xhc6Z2c9nHMAynB+0H7bXj8pd/ymc/bVm94LOut94KMz3pNb/nn9X4eBI3lhz55cs+mFqsugTtzzo3tyVn/xE+WNHz++6hJK8z+dhrd/ndSDXXbZZR3aV3vXZz04kC0TsiZtf1HUarXUarXS++guAwcOzAUXXFB1Gce1c2tcZKGNiRMnZtDQqqtoTL/Znk4JWvfsS17shP2cqJe31Ucd7THo5OTiC99ZdRm93kt90ylBq2OgYyZf+LacMqDqKnq31ufSKUGrY6BjJp1/bs47veoqGoNzAMpwftB+e/am04JW7wXljT1reC64QFtuGtMpO3ckglbeNGHihJw3aHDVZUC36Ft1AXCitmzZcth9RwtIj6XM+qwHtw0ePrzcB+BDt6/3Wa1A73LaoKor6L1OG1h1BSSOgSr165sMPrnqKnAMVMt7AVC1/v1i0FOFvA8DQOMRtNLwjtQ6uMyaqfu1d33WpG24W3Y26qHbHykoBqjKGadWXUHvdbrXvi74OVTn9CFJnz5VV4FjoDon9UuGGvQP1AHvBdVxPgYAjUfQSsM7UtDZkaB1xYoVbW4fbTZrcuRwt6M6c18AJ+ocXaoq47WvD2c1FTMr6X6Ogfow+ORkxJCqq+idzhrm9w9QH7wnV2fMsKorAADKchpHwztS696OrHl65513trndkfbDHbF58+ZueR6A9hhxajLwpKqr6J1c0KoP/fslo63hVokxI6qugP38PqqG1x2oF34fVWPEkGSwts0A0HAErTS8yZMnn/A+li5d2u71WU90BuqhwbAZrUA96dsnGePCSiWETPXDMVANF3Xrh2OgGo4BoF74fVQNrzs0sG3bqq4AqJCglR7h0LC1paWl1OPnzJnT5nZ712ftDNZoBerNuU7wu93QQcUX9eFcoXe369NHq7x64hiohgvsQL0YNbRYN5ru5X0AGlPrtm1pXfNU2/vWPJVW4Sv0Gv2rLgA6w5w5c9qEpS0tLe1ep3XGjBmHBbPHWp8VoKd7+1nJvb+tuore5e1nVV0BBzt/dNUV9D7NZyQDtC2vG+POSE7un7y+p+pKeo+hg5LRTVVXAVDo1zeZeGby6+errqR3eYdzAmgIexf+t2Tb9iRJ64YNaX3kV4dvtG1b9nzqM+lz6SXpM/rNE8whp6TfDf+8+woFuo0ZrfQIs2fPbjMDdcmSJe163Jw5c7J06dLD7u/K9VkPncF6pDVmAao0fqR1WrvbpLOrroCDjRiSnNVUdRW9y6QxVVfAwU7ql7zdgINudcGYYmY3QL3w3ty9hp1iwA00in13Ls6+H/ww++7/aTGTdciQo361rnkq++7/abH9nYvNcoUeyoxWeoxFixYdCEjnz5+fefPmHXP7GTNmZOnSpVm0aFFmzZrV5u+Ote5rZwejR2tRDFCVfn2L0dSPrK+6kt7hpH7JRIFG3Zk0JnmhVnUVvYfBBvVn0phk1bNVV9F7OAaAenOB30vdapIBN9AwTrrnB1WXANQZM1rpMaZPn56bbropSVKr1Y46K3Xx4sUZNmxYHn744axYseKwoLOpqemYbYc7OxgdMcIiWED9MYK9+0w8Mxlg6FvdcQx0n5GnJWecVnUVHOqdZ7ng211O7p9MOLPqKgDaOm1QMtblim5jwA0ANC6X9ehR5s2bl/Hjxx9oCTx+/PjMnDkz48ePz4oVK7J06dK0tLRk5syZWbBgQZqamtqs7ZqUX5+1VqudUM1mtAL16IKzk5P7Ja/vrbqSnu+Sc6uugCMZM7xoIbxZZ6cud+nYqivgSIYMTCaMSla/WHUlPd+FY4ruBgD15pKxyfrNVVfR8w0ZWCzfAgA0JjNa6XFmz56dV155JTfddFOampoyf/78zJ07Nw8//HBmzpyZtWvXZtGiRQcCzkPXaG3P+qzHmvF6PC0tLW1uW6MVqEcDT0qmjKu6ip7vlAHFBSzqT98+yXsmVF1Fz9e3T/Kut1VdBUfz3olVV9A7+F0D1Ksrmg0E6Q7vGp/09zoDQMMyo5Ueqamp6bhrtCbFbNRDg8/2zGidPHnygcdt2bKlVG2HzoA91nqwAFV6z4TkgaeqrqJnmzrexat6dkVz8oNHkz37qq6k55o0JmkaXHUVHM0FZydDByVbd1ZdSc91VlMy7oyqqwA4slMGFJ0nHmo5/rZ0TJ8+ybsNuAGAhmZGK73aobNZj7c+634Hz3ot2zp47dq1bW6fyOxYgK40Zngy7vSqq+i5+iR5t5l8dW3IQG1tu5oZk/WtX18Xf7vaeyZaCxeob96ru9YFZyfDTqm6CgDgRAha6dWWLFnS5nZ712e97LLL2tw+dFbssRy8bdn1YAG62/vOr7qCnuudZyenn1p1FRyPY6DrnDm0WAOU+vautyX9nTV2iUEnJ5edV3UVAMd27ohkrMGXXeYqnzUBoOE5ZaZX68j6rEnR7vfglr8rV65s93MeHLTOmTOn3Y8DqMIl5yajm6quomf66MVVV0B7nDuiaG9L5/vIRWbyNYLTBhlw0FWmvzMZcFLVVQAc3zU+t3aJCaMMOgOAnkDQSq/V0fVZ9zs4JD10Zmx7nrOpqSkzZ85s9/MBVKFv3+Rjl1RdRc8z5bzk7GFVV0F7fewSgWBnGzsiueicqqugvaZfkAwUCHaqoQJsoIFMPDM5/8yqq+h5rrnEZ0wA6AkErfRaHV2fdb/Zs2cf2P7OO+9s12Pmz59/4Pt58+a1+7kAqvTOs5LmM6quoufo17eYyUfjOHNocvm4qqvoWT52qQuLjeSUAcm0d1ZdRc/yOxclJ/evugqA9jP4snNddE5ynpbMANAjOLWj1/r2t7/d5nZH1ktdsmRJxo8fn1qtlrlz5x4zPK3VarnllluSFK2HZ8+eXfr5qD8bNrfk2z+el9XPPZwNW4rZyqOHN+eaK+fkmiu79me8cvXS/Ie/npU/mLkgV11Ubnb0/asW5+4Hb8/q5x7Otp21TDh7cs4c0ZwpE2aUqnv/v3/lmqV5beeWJMW///0XX5frPnhTqZqoX336JNdemtx2T9WV9AzvnmBt1kb0kYuSR9Ynb+ytupLG9/bR2uQ1oqvenvxsdbJ1Z9WVNL6RpyVXtH98Jz2QcwjnEI3onBHJpWOLz0OcmL59tGMGgJ5E0Eqv1dH1WQ/W3NycRYsWZdasWbn11ltz+eWXH7Ud8LRp01Kr1TJ58uQsW7asQzVTX77941uz8AdzM3nC9PzBzAWZMKZYt/f+VYvzF4tvzLd/PC/zZi/J6BGdcyVt285aXtuxJSvXLM3dD96eNc8XawO/tmNLqX38h2/Nyms7tuSaK+fkS5++Pdt21rJyzdL87Y9vyU9XLc63fzwvf/y5RQf+PUez4O65ufMnt2byhOlttt//77/7wds79d9Ptcadkbx3QvKzNVVX0tiGDXZRpVENO6X42f3v9i/LzhGc3D+ZdUXVVdARA9782S28r+pKGlufJNdfWXQ3oHdyDuEcopF9ckry5IZkx+tVV9LYZkxKRg2tugoAoLM4vaNXWrp0aWq1Wpv7OjKjNUlmzpyZFStWpLm5ObNmzcqsWbOycuXKA+uxzp8/P8OGDcvKlSsze/bsrFixIk1NTSf+j6BSdz84Pwt/MDfvu2hm5s1e0uaCwlUXzcw3v7gir+3cki/cNiXbdtZO+Pm+8I0p+eSfDMsXbpuSFWuW5P0XX9eh/dx0+7SMHt6cb35pRa65cnZGj2jOhDGTc90Hb8q3bl6XCWdPzoYtLfnCbVOy5rmjpwlz58/InT+59aj//ltnLzuwn87491Mfrr00GX5K1VU0ts9eaZ3DRnbV+cWgAzru45cmI4ZUXQUdNWlMcpk22ifk/W/3e6Q3cw7hHKLRnTYo+fRlVVfR2M4alsy4oOoqAIDOJGilV6nValm6dGnmzJlz2N8dOsO1jMmTJ2ft2rVZtGhRkmTWrFkZNmxYpkyZkttvvz2zZ8/O2rVrc/vtt3f4OagfGza35BvfKf4P/cnnFh1xm9EjmvPRqbOzbWct/2nxjSf8nLfOWZYl/7E1f/enr+RPPrcokyeUHxiw4O65SZIvzTzy/8Mhg5ryBzMXHLj9H74164jb3f3g/KxcUxwv/+ag7Q82YczkvO+imdm2s5aFbz4vjW/AScUsHDrmXW9Lzh9ddRWciL59i2PgpH5VV9KYJowqWmfT2D41pbjQTnlnnJp8VFeDXss5hHOInmLyecmFY6quojH17ZP87pVJf58lAaBHEbTSo9VqtYwfPz59+vRJnz59MmzYsMyYMSMtLS2HbTtnzpwD2/Xp0ydTpkwp/XwzZ87MokWLsnbt2rS2tuaVV17JihUrMm/evDQ3a33UU3z7x8VavMe7UPGxK4sLKT9dtfiER2QPGdR0Qo9Pkh8sn58zRzRnw+bD///vN2HM5AP/rg1bWnL3g/Pb/P22nbUDF4gmT5h+zLrOH3N5kuTu5fONSO9BJpyZvO/8qqtoPMNPST5x7E56NIiRpyUfu6TqKhrPwJOKGd19+1RdCSdq8IDkuqlVV9F4+vZJfvddRftseifnEM4heoo+fZLPXJEMGVB1JY3nwxcmY4ZXXQUA0Nmc5tHj7Q9V97frHT78yJ9qt2wp1qjZ31L4aNvB3cuLCwejhx87PD94XaG7H5yf6z54U5fWdSzbdtaybWctP121OD9dtThL/mPrUbd929mTD4w2v3/Volxz5ewDf7f//iQ55TgXbg6+iFT1v5/O9YlLk+e3JC0vVVvHy68lf/b9429TtZP7Jze8X8vgnuSq85NnNycPP11tHY1yDPTpk/zj92gZ3JNccHbyOxcm//BYtXU0yjGQJJ+6TMvg3s45RME5RM9w6qDkn1+V/OWyZO++amtplPeCSWOKtVkBgJ5H0EqP1tTUlNbWo58MQlkHrzk0esT4424/ZFBTtu2s5b5Hv13pRYLXdmxpc3vD5pY2F3EOdv45lx/zcfudNujYgxEO3v+Tz/2y3bVS//r3S/7ZVcl/+mHyyo7q6tizL3lxa3XP317/6N3FWkz0HH36JJ+Zmmx6LXlmc3V1NMoxcO0lyTvPrroKOtuHLkw21JJHn62uhkY5Bt79tuS9E6uugio5h3AO0RM1j0xmXp58e3m1dTTCe8GZQ4tzAp09AKBn0joYoITVzz184PujXWQ42P4R62ueX3mcLbvW6BHNuWbq7AwZ1JRrps4+Zu0HXwg5dXDnzOx+ZE3H10CmPp06MPn8+5OTrS90TL9zUXLROVVXQVc4uX8xk8Nalcd22bjkg++ougq6wv5WuGc1VV1JfRs/spjNSu/mHKJjnEPUv3e9LXmfgSTHNPhk3W0AoKcTtAKU8MLmtR1+bNVrDH1p5u35uz99JV+aefsxtzt45Pjbzm67qOTBF1de3dl2pPqhDh7JXvW/na4xZnjyj99rZPbRXD4u+ZD2YD1a0+DkRhfOjmrCqGItzz5+R/RYA05KbvxAcSxwuFGnJf/sfUUnCHo35xDOIXqy/2NK0VKew53UL/n8Vcnpp1ZdCQDQlQStACVsL3myf/Bo7kNbaNWjbTtrbUaOX3/1zW3+/uA1k473WlQ9Ap/uMWlMsfaiIKWtS8cmn71SCN0bnDMimfPBYoYrbxl3RjF74yQBU4837JTkX05Phprd3cbppyZfmJ4MGVh1JdQD5xDOIXqyfn2Tf/q+5PzRVVdSX/r3LT4LjR9VdSUAQFcTtAKUcLwR2MfSCCOy77j3lgN13vDReRkyqKnN3w8Z1JTPfKBYJ2rlmqXH/Df97b23tLndCP9+OuaSsck/eY9Qcb/JY4s1mPr5lNVrjDsj+RcfTAYIW5MkzWcU4fMAM317jTNOTX5/upmt+408rXg9hM/s5xzCOURPt3/m5tuFrUmK1+OGDwifAaC3cAkQoIOGDGyquoROtWFzS+78ya1JkvddNDPXffCmI2534zXzMuHNdmAL7557xG3ufnB+zjxkDadDL7jQs1wytmiP2Ntnr00dL2TtrZpHJl+YlpwyoOpKqnX+mcmcq7VT7o3OOC35VzOSEUOqrqRaZzUl/0rozDE4h3AO0VOd3L+YwXnROVVXUq0B/ZPZHxQ6A0Bv4jIgQAmnDXqrjde2XbVSj63niwTbdtYyd/6MJMUFkj/53KJjbn/rnGV530Uzc/fy+fnG4jnZsLklSXGhZcHdc3P3g7fn38xc0OV1U18uPKe4yN4bZ/D0SfLxS5PPTk36+nTVa409PfmDDyejh1ZdSTXed35xYdHM3t5rxJDk3/xOsT5vb3TROckXP5Sc2gvfBzk25xAF5xA9X/9+RRvhGRdUXUk1Tj81+YNe/D4IAL2VyyAAJZxS8kLHwWsqHbzWUr256fZp2bClJddMnZ0vzbz9uNsPGdSUP/ncoqxcvTTf/sm8fOG2Kdm2s5bRw5tzzZVz8s0vrWjT5mv08Oaj74we5dwRyb/5SPLf7kvWb666mu4x8KRindp3nl11JdSD009Nvvjh5K9/kTz+XNXVdI9+fZOZlyfvelvVlVAPThmQ/Iurk/+9Ivnp6qqr6T4fvrD40ka/8fzjW8Znw5aWTt3nvBuXZPLEt9YldQ5RcA7RO/Ttk1xzSXJmU/K3DyZv7K26ou5x/pnJP3lvMriXdzcBgN5I0ApQwlkjxh/4/uALIO1Rr6PR586fkTXPr8wff25RrrpoZqnHTp44vc1FpIPtH6GeJJMnHHkbeqahg5Lfn5F855fJg2urrqZrjTot+edXJaN66QxGjmzgScX/ix89ltzzeNLaWnVFXee0QcVFxfEjq66EetKvb/Lpy5OzhiXffbhnX2QfeFLRzeCSsVVXQkfdcM28PPnsLzttf6cNHpGJ51zW5j7nEG05h+gdppxXrOH933+avLK96mq61gffkXzsEsuHAEBvJWgFKGHimLcumhw82vpo9o+Or9fR2HPnz8jq5x7ON7+4IhPGTO7Ufa9+7uED31910axO3Tf176R+yWevTCaNSe5cnry6q+qKOlefPsnV70h+5yLr0nJkffskH7koecdZyd88kGx6teqKOt/lzcknJ5u5wdG9621FCH/HA8m6l6uupvO9fXRy3dRk2ClVV8KJuOqimaWDwrKcQ7Sfc4ie5dwRydxrkrseSX6+pupqOt+IIcU5j1bBANC7GWsFUMLBFxI2bD7+VL39F1LqcTT2n35rVjZsbjnqBZI1z63Mn37ryBc32nOBaMWaJUmKC0RHG7FOzzdpTDL3Y8ll46qupPOMOq1Yg+/aS4WsHN95pyf/9qPJ1e8sAvqeYOig5MYPJP/nu4SsHN/I04r1u/+PyT3nd+b+WaxzPihkpX2cQxScQ/ROA09KZl2RfGFaz/qd+b6JyU0fFbICAIJWgNLe9+aI94NHWx/JmudWHvj+mivndGlNZf3pt2blxc0t+eaXVmT0iCOPlF+5Zml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      "text/plain": [
       "<Figure size 2366.88x602 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise_amplifier = LocalFoldingAmplifier(\n",
    "    gates_to_fold = 2,\n",
    "    sub_folding_option = \"from_first\",\n",
    "    random_seed = None,\n",
    ")\n",
    "\n",
    "noisy_circuit = noise_amplifier.amplify_circuit_noise(circuit=circuit, noise_factor=2)\n",
    "display(noisy_circuit.draw(\"mpl\", plot_barriers=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b56a92b-e53e-43f0-892e-ed021cbba173",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Extrapolation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a200bbd-ad53-410c-aa64-e84facafa523",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "![Extrapolation](../media/extrapolation.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9466a1d0",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Custom ZNE strategies\n",
    "\n",
    "The current implementation of `zne` allows us build custom noise amplification, and extrapolation techniques:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4182d098",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zne.extrapolation import Extrapolator, ReckoningResult\n",
    "from zne.noise_amplification import CircuitNoiseAmplifier\n",
    "\n",
    "\n",
    "############################  NOISE AMPLIFIER  ############################\n",
    "class CustomAmplifier(CircuitNoiseAmplifier):\n",
    "    def amplify_circuit_noise(self, circuit, noise_factor):\n",
    "        return circuit.copy()  # Dummy, nonperforming\n",
    "\n",
    "\n",
    "############################  EXTRAPOLATOR  ############################\n",
    "class CustomExtrapolator(Extrapolator):\n",
    "    @property\n",
    "    def min_points(self):\n",
    "        return 2\n",
    "    \n",
    "    def _extrapolate_zero(self, x_data, y_data, sigma_x, sigma_y):\n",
    "        value = 1.0\n",
    "        std_error = 1.0\n",
    "        metadata = {\"meta\": \"data\"}\n",
    "        return ReckoningResult(value, std_error, metadata)  # Dummy, nonperforming"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "833299e7",
   "metadata": {},
   "source": [
    "Which we then provide during instantiation through a `ZNEStrategy` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d29bb8dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"values\": [\n",
      "    1.0\n",
      "  ],\n",
      "  \"metadata\": [\n",
      "    {\n",
      "      \"zne\": {\n",
      "        \"noise_amplification\": {\n",
      "          \"noise_amplifier\": \"CustomAmplifier\",\n",
      "          \"noise_factors\": [\n",
      "            1,\n",
      "            3\n",
      "          ],\n",
      "          \"values\": [\n",
      "            0.0234375,\n",
      "            0.013671875\n",
      "          ],\n",
      "          \"variance\": [\n",
      "            0.99945068359375,\n",
      "            0.9998130798339844\n",
      "          ],\n",
      "          \"shots\": [\n",
      "            1024,\n",
      "            1024\n",
      "          ]\n",
      "        },\n",
      "        \"extrapolation\": {\n",
      "          \"extrapolator\": \"CustomExtrapolator\",\n",
      "          \"variance\": 0.0,\n",
      "          \"metadata\": null\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "zne_strategy = ZNEStrategy(\n",
    "    noise_amplifier=CustomAmplifier(),\n",
    "    noise_factors=(1, 3),\n",
    "    extrapolator=CustomExtrapolator(),\n",
    ")\n",
    "    \n",
    "job = estimator.run(circuit, observable, zne_strategy=zne_strategy)    \n",
    "result = job.result()\n",
    "\n",
    "print(EstimatorResultEncoder.dumps(result, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ec233ea-872f-41d7-9640-bb4eab7e2b99",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Conclusions and news"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d93f10c-3960-4870-a51b-7082d3dbb010",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Runtime integration with exclusive strategies\n",
    "You can now access cutting edge ZNE strategies for enhanced error mitigation through _[Qiskit Runtime](https://qiskit.org/documentation/partners/qiskit_ibm_runtime/)_.\n",
    "\n",
    "![source model](../media/runtime.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bed57205-29f3-480d-8690-ca935bd9702c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.22.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.10.5</td></tr><tr><td>Python compiler</td><td>Clang 13.1.6 (clang-1316.0.21.2.5)</td></tr><tr><td>Python build</td><td>main, Jun 23 2022 17:15:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Fri Oct 14 22:08:30 2022 EDT</td></tr></table>"
      ],
      "text/plain": [
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      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter  # pylint: disable=unused-import,wrong-import-order\n",
    "\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  }
 ],
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   "language": "python",
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